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So, What Exactly is a Data Scientist?

What is a data scientist?

During our 2020 virtual user conference, one of our sessions went through  eoStar Analytics. During the session our very own data scientist talked about the future of data, analytics, and how to use information to make more informed decisions for your business. But there was one question on a lot of people’s minds, what is a data scientist?

The job is to the fulfill the formula of Problems + Data => Models + Insights. They are able to map expert knowledge and context into real-life solutions. An unofficial definition of a data scientist has become: someone who knows more about math and statistics than a computer scientist and more about computer science than a mathematician or statistician.

“A Data Scientist is a professional who extensively works with Big Data in order to derive valuable business insights from it. Over the course of a day, the Data Scientist (DS) has to assume many roles: a mathematician, an analyst, a computer scientist, and a trend spotter.” – IntelliPaat[1]

Data Science is a rapidly changing field, so anyone that takes on the role of data science needs to be able to keep up with the latest research and techniques. Due to the cloudiness of what exactly a data scientist is and does, there is an increasing overlap between data science and machine learning. Machine learning includes things like computer vision, speech recognition, neural networks, etc. The person that implements the models and insights data scientists come up with is traditionally called a Machine Learning Engineer (MLE), but this can be interchangeable with “Data Scientist” at a lot of organizations.

A typical workflow for a data scientist or machine learning engineer is as follows:

  1. Scoping
    1. Working closely with the product team, the DS will gather the requirements around a particular business problem and determine if a solution is possible given the available data.
  2. Research
    1. This process involves gathering the necessary datasets, cleaning them, and experimentation on the data. This is the phase where the DS really gets to understand the relationships within the data as well as outside of the data. Research also goes into figuring out which approach will be best to solve the problem.
  3. Modeling
    1. Once a few approaches are selected to explore, the DS fits the various models to the data and benchmarks are calculated to determine which system will work best in a real-life scenario. The goal of this phase is to converge on a single approach to deploy.
  4. Deployment
    1. Finally, the model or system is deployed and put into production so that it can be integrated into the existing software ecosystem.

As the beverage industry continues to evolve with new SKU’s, expanded product offerings, and tightening inventory, data and analytics are becoming more essential than before. At eoStar we are fortunate to have a team focused on turning your data into insights and helping you turn those insights into action. To learn more about how our products are using data and analytics to improve your business, talk to one of our sales experts.


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